Interdisciplinary School of Scientific Computing Syllabus for M. Sc. (Scientific Computing) LIST OF COURSES _____________________________________________________________________ Semester – I Semester – II SC-101 Programming Languages and principles I SC-201 Programming Languages and SC-102 Software Engineering Principles II SC-103 Advanced Database Management Concepts SC-202 Operating Systems SC-104 Mathematics for Scientific Computing SC-203 Elective course SC-105 Computational Lab-I SC-204 Numerical Methods for Scientific Computing-I SC-205 Computational Lab-II ___________________________________________________________________________ Semester – III Semester – IV SC – 301 Network Concepts SC-401 R & D/Industrial Project SC – 302 Scientific Visualization SC – 303 Elective course SC – 304 Numerical Methods for Scientific Computing-II SC – 305 Elective course ___________________________________________________________________________ Elective course will be offered from the following list. Elective Courses El-1 Application of Computer to Chemistry. El -6 Modelling of Biological Systems El-2 Parallel Processing and Grid Computing. El -7 Artificial Intelligence El-3 Statistical Computing. El -8 Quality Assurance and Software Testing El-4 Applications of Computer to Physics. El -9 Soft Computing El-5 Biological Sequence Analysis El-10 Design concepts and Modeling - 1 -
Interdisciplinary School of Scientific Computing Detailed Syllabus: SC – 101 Principles of Programming Languages 1. Introduction and Motivation [8 hrs] Idea of analyzing an algorithm through examples, introduction to some notations, comparison of algorithms, notions of space and time efficiency and motivation for algorithm design methods, demonstration of algorithm analysis for some suitable example algorithm, say merge sort. 2. Algorithm Analysis Techniques [12 hrs] (a) Asymptotic Analysis Detailed coverage of asymptotic notations and analysis. Big Omicron, Big Theta, Big Omega, Small theta, Small omega. Comparison of the Insertion Sort and the Merge Sort Algorithms. (b) Recurrence Analysis Introduction to Recurrence equations and their solution techniques (Substitution Method, Recursion Tree Method, and the Master Method), Proof of the Master Method for solving Recurrences. Demonstration of the applicability of Master Theorem to a few algorithms and their analysis using Recurrence Equations. (Example algorithms: Binary Search, Powering a number, Strassen's Matrix Multiplication) (c) Analysis of more Sorting algorithms: Quick Sort and Counting Sort 3. Algorithm Design Techniques [12 hrs] (a) Types of Algorithms (b) Dynamic Programming Introduction and the method for constructing a DP solution, illustrative problems, e.g. assembly line scheduling problem using DP, or solution for the matrix chain multiplication. (c) Greedy Algorithms Greedy vs. DP, methodology, illustrative problems, e.g. the knapsack problem using a greedy technique, or activity selection Problem. Construction of Huffman Codes. (d) Backtracking Introduction to recursion, solving the 0-1 Knapsack problem using backtracking, pruning in backtracking and how it speeds up the solution for the 0-1 Knapsack problem. (e) Branch and Bound Description and comparison with backtracking, the FIFO B&B and the Max Profit B&B using the 0-1 Knapsack problem 4. Graph Theory [12 hrs] (a) Breadth First and Depth First Search Algorithms (b) Minimum Spanning Trees, Kruskal's Algorithm (c) Minimum Spanning Trees, Prim's Algorithm (d) Properties of Shortest paths. (e) Dijkstra's Algorithm (f) Bellman Ford Algorithm 5. NP-Completeness [12 hrs] (a) Polynomial time (b) Polynomial time verification (NP problems) (c) Concept of NP-Hard with example (Halting problem) (d) NP-Completeness and Reducibility (without proof) (e) Some NP-Complete problems (f) Overview of showing problems to be NP-Complete - 2 -
Interdisciplinary School of Scientific Computing Text Reference: 1. Introduction to Algorithms, T.H.Cormen, C.E.Leiserson, R.L.Rivest, Prentice Hall India, 2002. 2.The Art of Computer Programming, Vols. 1 and 3, D.E.Knuth, Addison Wesley, 1998. 3.Design and Analysis of Algorithms, A.V.Aho, J.E.Hopcroft, J.D.Ullman, Addison Wesley, 1976. 4.Fundamentals of Computer Algorithms, E.Horowitz, S.Sahni, Galgotia Publishers, 1984. 5.Data Structures and Algorithms, Vols.1 and 2, K.Melhorn, Springer Verlag, 1984. 6.The Analysis of Algorithms, P.W.Purdom, Jr. and C.A.Brown, Holt Rhinehart and Winston, 1985. SC-102 SOFTWARE ENGINEERING 1. Introduction to software engineering [4 hrs] (a) A generic view of process (b) Process models (c) An agile view of process 2. Software engineering practice (a) Software Engineering Practices [3 hrs] Planning practice, communication practice, construction practice, modeling practice (b) System Engineering [3 hrs] System engineering hierarchy, system modeling (c) Requirement Engineering [4 hrs] Requirements engineering tasks, initiating the requirement process, finding requirements, building analysis model, developing use cases (d) Design Engineering [4 hrs] Design process and design quality, design concepts, design models, (e) Performing user interface design [4 hrs] The Golden rules, User interface analysis and design, interface analysis, (f) Testing strategies [3 hrs] Test strategies for conventional software, validation testing, system testing, debugging, different testing types like white box, black box etc. (g) Project scheduling [4 hrs] Basic principles, finding task set, defining task network, scheduling (h) Risk management [4 hrs] Reactive Vs. proactive risk, risk identification, projection and refinement. RMMM plan 3. Case study [15 hrs] Initiating, analyzing, designing and testing of a project 4. Advance topics in software engineering [15 hrs] - 3 -
Interdisciplinary School of Scientific Computing Text Reference: 1. Software Engineering a Practitioner’s Approach Roger S. Pressman McGraw – Hill 2. Software Engineering Richard Fairley Tata McGraw Hill 3. Software Engineering David Gustafson 4. Practical Guide in Structured System Design Meilier Page SC-103 : ADVANCED DATABASE MANAGEMENT CONCEPTS 1. Review of Database Management Concepts [6 hrs] Types of database, Normalization (1Nf,2Nf,3Nf, BCNF,5NF), data models, constraints, ER-model, Introduction to Scientific Database 2. Data Storage & Indexing techniques [6 hrs] Architecture of DBMS, Storage of data on disk & files, File organization & type of file organization, Advanced storage devices – RAID, Type of Indexing 3. SQL query optimization [8 hrs] Implementation and Evaluation of relational operations, Types of joins and join algorithms, Select of appropriate index, database workload, which index to create, guidelines for index selection, co-clustering, index on multiple attributes, Cost estimation and cost based optimization, Plan evaluation, Tuning conceptual schema 4. Review of Transaction Management & Security [6 hrs] Introduction to transaction management (ACID property, states of transaction), Concurrency control (locked based concurrency control, optimistic concurrency control, timestamp based concurrency control, deadlock detection & handling), Crash recovery (log based recovery, shadow paging), Security (identification & authentication , authorization matrix, views, encryption techniques, statistical database, polyinstantiation, role of DBA) 5. Parallel and Distributed Database (DDB) [20 hrs] Introduction to Parallel database, Architecture of Parallel database, Parallel query evaluation & optimization, Introduction to DDB, DDBMS Architecture, Storing data in DDBMS, Catlog Management, Query Processing(non-join queries, joins – fetch as needed, ship to one site, semijoins), Updating database, Transaction Management, Concurrency Control, Recovery 6. Data Mining [8 hrs] Introduction to data mining, Knowledge Representation Using Rules - Association and Classification Rules, Sequential Patterns, Algorithms for Rule Accessing ASSIGNMENT: Case study which should include following things - ER diagram, functional schema, Normalization, indexing Text Reference: 1. Database Management System Raghuramkrishnan, Gehrke (3 rd Edition) McGraw Hill - 4 -
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